Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.
Version: | 1.0.0 |
Depends: | R (≥ 2.10) |
Imports: | data.table, methods, rlang (≥ 1.0.0), nloptr |
Suggests: | ggplot2 (≥ 3.4.0), testthat (≥ 3.0.0) |
Published: | 2024-09-13 |
DOI: | 10.32614/CRAN.package.power.transform |
Author: | Alex Zwanenburg [aut, cre], Steffen Löck [aut], German Cancer Research Center (DKFZ) [cph] |
Maintainer: | Alex Zwanenburg <alexander.zwanenburg at nct-dresden.de> |
BugReports: | https://github.com/oncoray/power.transform/issues |
License: | EUPL |
URL: | https://github.com/oncoray/power.transform |
NeedsCompilation: | no |
Materials: | NEWS |
CRAN checks: | power.transform results |
Reference manual: | power.transform.pdf |
Package source: | power.transform_1.0.0.tar.gz |
Windows binaries: | r-devel: power.transform_1.0.0.zip, r-release: power.transform_1.0.0.zip, r-oldrel: power.transform_1.0.0.zip |
macOS binaries: | r-release (arm64): power.transform_1.0.0.tgz, r-oldrel (arm64): power.transform_1.0.0.tgz, r-release (x86_64): power.transform_1.0.0.tgz, r-oldrel (x86_64): power.transform_1.0.0.tgz |
Reverse suggests: | familiar |
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